Philippine Relief Efforts Aided by Visual Analytics


In giving advice on how to build a compelling analytics resume, hiring managers and recruiters we've talked to at All Analytics typically include volunteer work among their suggestions. Plenty of non-profit organizations are lacking resources needed to make sense of, and get value from, their data.

Today, I've learned that SAS, this site's sponsor, is leading by example with noteworthy analytics volunteerism of its own.

Typhoon Haiyan showing
annual characteristics on
Nov. 7, 2013 (source: NASA).
Typhoon Haiyan showing
annual characteristics on
Nov. 7, 2013 (source: NASA).

In November 2013, as you no doubt recall, Typhoon Haiyan unleashed its fury on the Philippines. The devastation was horrific, with USAID reporting 6,201 dead, 4.1 million displaced, and 1.1 million homes damaged or destroyed.

The situation remains dicey, at best, as the International Organization for Migration (IOM) noted in an evidence-based May report on the continued impact. As noted on the Philippine Response Blog:

Now, more than six months on, more than two million people are still without adequate shelter or durable housing, with over 26,000 living in temporary sites (evacuation centres, tent cities, spontaneous settlements and bunkhouses). Many face prolonged uncertainty about whether they will be allowed to settle back in their former homes -- most of which lie in designated "no-safe" zones -- and what plans there are for their permanent relocation, with a lack of transparent information a key concern.

In spite of the wealth of information generated, it has been difficult to form a coherent understanding of the evolving and complex displacement situation [following Typhoon Haiyan]...," says Alfredo Zamudio, director of the Internal Displacement Monitoring Centre, the reportís co-authors.

And that's where SAS, via a pro-bono pilot project, fits in.

In crises such as the one wrought by Typhoon Haiyan, IOM, an inter-governmental organization, works on behalf of the displaced -- managing shelters and coordinating operational efforts at evacuation centers, camps, and schools. Central to those efforts is a tracking and monitoring system called Displacement Tracking Matrix, or DTM.

As part of the Philippine relief effort, IOM shared DTM data with government and humanitarian partners, as well as with SAS for use in a pro-bono project using SAS Visual Analytics. Another Philippine Response Blog post described the project, which started with Excel data, as such:

The Philippines office of SAS Visual Analytics organized and analysed the data to identify shelters which faced the most critical health risks. Within minutes of the first data being uploaded, a map emerged showing shelters experiencing a dangerous mix of overcrowding, unsafe drinking water and solid waste disposal problems. This allowed IOM to pinpoint sites where high number of families still lived in makeshift shelters or dramatic growth of certain vulnerable populations in a short amount of time.

A SAS Visual Analytics display of demographic data on people displaced by Typhoon Haiyan.
A SAS Visual Analytics display of demographic data on people displaced by Typhoon Haiyan.

Further, as SAS described in a press release, text analysis showed upper respiratory and cold symptoms to be the most common health complaints. "But more alarming were higher concentrations of diarrhea, fever, and skin disease among older people living in evacuation centers in Leyte. The DTM-generated data was shared with local health authorities to address these health needs."

The use of Visual Analytics fit into IOM's overall goal to modernize disaster response. In a prepared statement, Ambassador William Swing, IOM director general, said:

We have been working to enhance preparedness by developing practical tools for government officials, humanitarian organizations and affected communities. The SAS collaboration provided the right tool at the right time. We, our beneficiaries and partners are all grateful for the partnership and technology.

Text analysis also came into play for assessing reports coming in via social media conversations from areas like Guiuan, where phone lines had been knocked out of service. For example, an analysis of more than 10,000 tweets "indicated total structural devastation in Guiuan," SAS said. However, the same analysis confirmed Red Cross efforts to distribute food and the presence of an Australian emergency medical team. "It further shed light on what the local hospital needed most -- essential medicines such as antibiotics and fuel for generators, so that critical hospital services could continue to meet increased health care demands."

It seems to me this is an example of volunteerism at its best -- bringing together the data-savvy with sophisticated tools enabling real-time analysis and insight into critical recovery services. Do you have your own examples of analytical-minded volunteerism? We'd love to hear your story, so share below.

— Beth Schultz, Circle me on Google+ Follow me on TwitterVisit my LinkedIn pageFriend me on Facebook, Editor in Chief, AllAnalytics.com

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Beth Schultz, Editor in Chief

Beth Schultz has more than two decades of experience as an IT writer and editor.  Most recently, she brought her expertise to bear writing thought-provoking editorial and marketing materials on a variety of technology topics for leading IT publications and industry players.  Previously, she oversaw multimedia content development, writing and editing for special feature packages at Network World. In particular, she focused on advanced IT technology and its impact on business users and in so doing became a thought leader on the revolutionary changes remaking the corporate datacenter and enterprise IT architecture. Beth has a keen ability to identify business and technology trends, developing expertise through in-depth analysis and early adopter case studies. Over the years, she has earned more than a dozen national and regional editorial excellence awards for special issues from American Business Media, American Society of Business Press Editors, Folio.net, and others.

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Re: Coordination
  • 6/11/2014 12:38:56 PM
NO RATINGS

@Phoenix -- first off, kudos to you for volunteering your services to help in the relief effort. Did you do so from a technical skills perspective or more generally?

Second, did you see any evidence that relief organizations were collecting and analyzing data at all? 2004 predates much of the advanced analytics of today, but I'm wondering what might have been done with the tools available at the time?

I would imagine there are lots of lessons to be learned from today's recovery efforts, and from what I've learned in reading about this project, the United Nation is really pushing for more data to be made available to the public -- which in turn will lead, hopefully, to more analytical insight. That's a great direction to be moving in.

 

Coordination
  • 6/10/2014 9:55:27 PM
NO RATINGS

The 2004 tsunami relief efforts were really difficult to coordinate. If big data analytics were available at the time it would have helped a lot. I know this first hand since I volunteered to coordinate relief efforts in Sri Lanka. Some of the projects are still on going. You can imagine the scale of devastation and the effort it takes to bring things back to normal or as normal as it can ever be. Internet and email helped a lot to organise everything. But it was a daunting task with so many people in need.

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